Modified elite chaotic artificial fish swarm algorithm for PAPR reduction in OFDM systems

© 2014 IEEE. Orthogonal frequency division multiplexing (OFDM) is a leading technology in the field of broadband wireless communications. In OFDM systems, a high peak-to-average power ratio (PAPR) is a critical issue, which may cause a nonlinear distortion and reduce power efficiency. To reduce the PAPR, partial transmit sequences (PTS) technique can be applied to the transmit data. However, the phase factor sequence selection in PTS technique is a non-linear optimization problem and it suffers from high complexity and memory use when there is a large number of non-overlapping sub-blocks in one symbol. In this paper a novel modified elite chaotic artificial fish swarm algorithm for PTS method (MECAFSA-PTS) is proposed to generate the optimum phase factors. The MECAFSA-PTS method is evaluated with extensive simulations and its performance is compared with quantum evolutionary and selective mapping algorithms. Our results show that the proposed MECAFSA-PTS algorithm is efficient in PAPR reduction

Modified Alternative-signal Technique for Sequential Optimisation for PAPR Reduction in OFDM-OQAM Systems

A modified alternative signal technique for reducing peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing systems employing offset quadrature amplitude modulation (OFDM-OQAM) is proposed. Lower PAPR reduces the complexity of digital to analog converters and results in increasing the efficiency of power amplifiers. The main objective of the algorithm is to decrease PAPR with low complexity. The alternative signal method involves the individual alternative signal (AS-I) and combined alternative signal (AS-C) algorithms. Both the algorithms decrease the peak to average power ratio of OFDM-OQAM signals and AS-C algorithm performs better in decreasing PAPR. However the complexity of AS-C algorithm is very high compared to that of AS-I. To achieve reduction in PAPR with low complexity, modified alternative signal technique with sequential optimisation (MAS-S) is proposed. The quantitative PAPR analysis and complexity analysis of the proposed algorithm are carried out. It is demonstrated that MAS-S algorithm simultaneously achieves PAPR reduction and low complexity

On PAPR Reduction of OFDM using Partial Transmit Sequence with Intelligent Optimization Algorithms

In recent time, the demand for multimedia data services over wireless links has grown up rapidly. Orthogonal Frequency Division Multiplexing (OFDM) forms the basis for all 3G and beyond wireless communication standards due to its efficient frequency utilization permitting near ideal data rate and ubiquitous coverage with high mobility. OFDM signals are prone to high peak-to-average-power ratio (PAPR). Unfortunately, the high PAPR inherent to OFDM signal envelopes occasionally drives high power amplifiers (HPAs) to operate in the nonlinear region of their characteristic leading out-of-band radiation, reduction in efficiency of communication system etc. A plethora of research has been devoted to reducing the performance degradation due to the PAPR problem inherent to OFDM systems. Advanced techniques such as partial transmit sequences (PTS) and selected mapping (SLM) have been considered most promising for PAPR reduction. Such techniques are seen to be efficient for distortion-less signal processing but suffer from computational complexity and often requires transmission of extra information in terms of several side information (SI) bits leading to loss in effective data rate. This thesis investigates the PAPR problem using Partial Transmit Sequence (PTS) scheme, where optimization is achieved with evolutionary bio-inspired metaheuristic stochastic algorithms. The phase factor optimization in PTS is used for PAPR reduction. At first, swarm intelligence based Firefly PTS (FF-PTS) algorithm is proposed which delivers improved PAPR performance with reduced searching complexity. Following this, Cuckoo Search based PTS (CS-PTS) technique is presented, which offers good PAPR performance in terms of solution quality and convergence speed. Lastly, Improved Harmony search based PTS (IHS-PTS) is introduced, which provides improved PAPR. The algorithm has simple structure with a very few parameters for larger PTS sub-blocks. The PAPR performance of the proposed technique with different parameters is also verified through extensive computer simulations. Furthermore, complexity analysis of algorithms demonstrates that the proposed schemes offer significant complexity reduction when compared to standard PAPR reduction techniques. Findings have been validated through extensive simulation tests

A Modified Shuffled Frog Leaping Algorithm for PAPR Reduction in OFDM Systems

© 2015 IEEE. Significant reduction of the peak-to-average power ratio (PAPR) is an implementation challenge in orthogonal frequency division multiplexing (OFDM) systems. One way to reduce PAPR is to apply a set of selected partial transmission sequence (PTS) to the transmit signals. However, PTS selection is a highly complex NP-hard problem and the computational complexity is very high when a large number of subcarriers are used in the OFDM system. In this paper, we propose a new heuristic PTS selection method, the modified chaos clonal shuffled frog leaping algorithm (MCCSFLA). MCCSFLA is inspired by natural clonal selection of a frog colony, it is based on the chaos theory. We also analyze MCCSFLA using the Markov chain theory and prove that the algorithm can converge to the global optimum. Simulation results show that the proposed algorithm achieves better PAPR reduction than using others genetic, quantum evolutionary and selective mapping algorithms. Furthermore, the proposed algorithm converges faster than the genetic and quantum evolutionary algorithms

A Comparison Study of PAPR Reduction in OFDM Systems Based on Swarm Intelligence Algorithms

Optimization algorithms have been one of the most important research topics in Computational Intelligence Community. They are widely utilized mathematical functions that solve optimization problems in a variety of purposes via the maximization or minimization of a function. The swarm intelligence (SI) optimization algorithms are an active branch of Evolutionary Computation, they are increasingly becoming one of the hottest and most important paradigms, several algorithms were proposed for tackling optimization problems. The most respected and popular SI algorithms are Ant colony optimization (ACO) and particle swarm optimization (PSO). Fireworks Algorithm (FWA) is a novel swarm intelligence algorithm, which seems effective at finding a good enough solution of a complex optimization problem. In this chapter we proposed a comparison study to reduce the high PAPR (Peak-to-Average Power Ratio) in OFDM systems based on the swarm intelligence algorithms like simulated annealing (SA), particle swarm optimization (PSO), fireworks algorithm (FWA), and genetic algorithm (GA). It turns out from the results that some algorithms find a good enough solutions and clearly outperform the others candidates in both convergence speed and global solution accuracy

Chicken Swarm Optimization for PTS based PAPR Reduction in OFDM Systems

Partial transmit sequence (PTS) is a well-known PAPR reduction scheme for the OFDM system. One of the major challenge of this scheme is to find an optimal phase vector using exhaustive search over all the allowed phase factor combinations. This leads to increased search complexity which grows exponentially as the number of sub-blocks is increased. In this paper, chicken swarm optimization (CSO) based PTS system is designed that aims to find an optimal solution in less number of average iterations and therefore results in reduced computational complexity of the system. We have proposed two categories of the algorithm: (i) CSO-PTS system without threshold limit on PAPR (ii) CSO-PTS system with threshold limit on PAPR. Both the schemes offer effective trade-offs between the computational complexity and the PAPR reduction capability of the system. Simulation results confirm that our proposed schemes perform well in terms of low computational complexity, lesser number of average iterations and improved PAPR reduction capability of the OFDM signal without any loss in BER performance of the system